|Subject||Use of long-term water quality forecast in decision-making for dry-season water quality management (Korean)|
|Author||Dr. Jaepil Cho||Date||2016.01.01|
Demand from water resource managers is increasing for seasonal climate prediction information with a lead time of several months, as this information can provide key knowledge on issues like long-term dam inflow and water quality prediction information. Long-term water quality forecasts are particularly important in watershed management because they allow for these managers to implement proactive water quality control management techniques.
The objectives of this study are: (1) to develop a hybrid downscaling technique for predicting long-term precipitation and temperature on the Korean peninsula, by considering both the multi-model based prediction data provided by the APEC Climate Center (APCC) and the statistical prediction information based on teleconnection for water resources management; and (2) to evaluate the applicability of the seasonal forecast information in long-term future water quality predictions by using the predicted climate information as inputs to water quality modeling.
APCC produces climate prediction information utilizing a multi-climate model ensemble (MME) technique. In this study, four different downscaling methods, in accordance with the degree of seasonal climate prediction information utilization, were developed in order to improve predictability and to refine the spatial scale. These methods include: 1) the Simple Bias Correction (SBC) method, which directly uses APCC’s dynamic prediction data with a 3 to 6 month lead time; 2) the Moving Window Regression (MWR) method, which indirectly utilizes dynamic prediction data; 3) the Climate Index Regression (CIR) method, which predominantly uses observation-based climate indices; and 4) the Integrated Time Regression (ITR) method, which uses predictors selected from both CIR and MWR. Then, sampling-based temporal downscaling was conducted using the Mahalanobis distance method in order to create hourly weather inputs to the HSPF-EFDC modeling system in the National Institue of Environmental Research (NIER).
The SBC method shows the highest model selection for temperature forecast, while the MWR method shows the highest selection for precipitation prediction. For both precipitation and temperature prediction, there were difficulties when selecting models for the period of January to June. The greatest Temporal Correlation Coefficient (TCC) values occurred during the months of December and September for precipitation and temperature, respectively.
A framework for delivering water quality prediction information for decision-making was presented by considering four different levels of information including: 1) long-term water quality prediction information, 2) risk index information for each river segment, 3) best management practice information, and 4) economic analysis results. For the long-term water quality prediction information, the modeling-based predictability of water quality within the Yeongsan Basin was evaluated by using predicted data for January - June, 2014. The risk index, concerning the mass proliferation of blue-green algae in the mainstream, was considered by using the similar concept utilized for the vulnerability assessment of climate change. This consists of three components including exposure, sensitivity, and adaptive capacity. In terms of the best management practices, the scenario to control the total phosphorus concentration in the effluent of sewage treatment facilities, was selected. Then HSPF-EFDC was applied to assess the degree of water quality improvement through the management activities.
A comparison of observed Chlorophyll-a concentration (Observed-Chla), simulated Chlorophyll-a concentration using observed weather data (EFDC-Observed), and simulated Chlorophyll-a concentration using forecasted MME data (EFDC-Forecast), shows that more uncertainty occurs in water quality modeling procedures than the seasonal forecasting procedure. Risk assessments showed that risk due to algae increased in the downstream section. Even though the downstream section has higher water storage in agricultural reservoirs and lower Chlorophyll-a concentrations, the risk index was highest there mainly due to sensitive index in consideration of water demands.
For a legal and institutional approach for long-term water quality prediction, it was suggested to make amendments to the provisions of water quality prediction and response measures regarding: the definition of water quality prediction, predicting water quality constituents, target area, announcement, and configuration of the water quality council. In addition, legal and institutional improvement in the higher level law was recommended in order to take advantage of the long-term prediction information for the preservation of ecology and water quality. It was also proposed that content on the application of various water quality management techniques, which utilize the long-term climate prediction information for prevention purposes, should be added to the water and environment management guidelines and plan. Regarding the construction of the long-term water quality prediction system, a GIS-based prediction system was suggested to improve the utilization of spatial information as proactive response and preventative measures, such as spatial distribution of pollutant sources and past water quality trends related to water quality management.
Keyboards: Seasonal Forecast, Long-term Prediction, Water Quality, Downscaling, Climate Index, Multi-Model Ensemble (MME), Teleconnection, Algae